{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03c03c94-5464-4db4-a65a-b0ce085f6e9b",
   "metadata": {},
   "source": [
    "# Mars: solar occultation with AOTF spectrometer\n",
    "\n",
    "In this example, we show how archNEMESIS can be used to calculate forward models for solar occultation measurements made with spectrometers that combine an echelle grating for high-resolution spectroscopy, together with an acousto-optic tunable filter (AOTF) for the separation of diffraction orders. Specifically, we are going to model spectral signatures of CO$_2$ in a spectral range between $\\sim$6500-7000 cm$^{-1}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a375d6fa-f37a-4158-8999-d070773f621c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import archnemesis as ans\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9bf6cca-b87d-4dbf-85b2-a0bc78ba2235",
   "metadata": {},
   "source": [
    "## 1. Input files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "16eb126f-95e4-4558-ad69-12f65eb0a92a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING :: read_hdf5 :: Layer_0.py-379 :: When reading file \"acsnir_fm.h5\", could not find element \"Layer/BASEH\" setting returned value to \"None\"\n",
      "WARNING :: read_hdf5 :: Layer_0.py-380 :: When reading file \"acsnir_fm.h5\", could not find element \"Layer/BASEP\" setting returned value to \"None\"\n",
      "WARNING :: read_hdf5 :: Layer_0.py-381 :: When reading file \"acsnir_fm.h5\", could not find element \"Layer/TAUTOT\" setting returned value to \"None\"\n",
      "WARNING :: read_header :: Spectroscopy_0.py-564 :: self.NGAS=2 self.LOCATION=PathRedirectList(['/exomars/retrievals/nemesis/spectroscopy/LBLtables/ACSNIR/ACSNIR_WN_CO2_iso1.lta', '/exomars/retrievals/nemesis/spectroscopy/LBLtables/ACSNIR/ACSNIR_WN_CO2_iso2.lta'], redirects = {})\n",
      "INFO :: read_apr :: Variables_0.py-823 :: \n",
      "Variables_0 :: read_apr :: varident [1 0 2]. Constructed model \"Model2\" (id=2)\n",
      "INFO :: read_apr :: Variables_0.py-826 ::   Model2:\n",
      "  |- id : 2\n",
      "  |- parent classes: PreRTModelBase\n",
      "  |- description: In this model, the atmospheric parameters are scaled using a\n",
      "  |               single factor with  respect to the vertical profiles in the\n",
      "  |               reference atmosphere\n",
      "  |- n_state_vector_entries : 1\n",
      "  |- state_vector_slice : slice(0, 1, None)\n",
      "  |- state_vector_start : 0\n",
      "  |- target : 0\n",
      "  |- Parameters:\n",
      "  |  |- scaling_factor :\n",
      "  |  |  |- slice : slice(0, 1, None)\n",
      "  |  |  |- unit : PROFILE_TYPE\n",
      "  |  |  |- description: Scaling factor applied to the reference profile\n",
      "  |  |  |- apriori value : 1.0\n"
     ]
    }
   ],
   "source": [
    "runname = 'acsnir_fm'\n",
    "\n",
    "#Reading the input files\n",
    "Atmosphere,Measurement,Spectroscopy,Scatter,Stellar,Surface,CIA,Layer,Variables,Retrieval,Telluric = ans.Files.read_input_files_hdf5(runname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d451b564-fa4a-4cb0-9ccb-ecc32a5d3656",
   "metadata": {},
   "source": [
    "## 2. Introduction to the AOTF filter function\n",
    "\n",
    "The AOTF of the instrument is, as it name suggests, a tunable filter characterised by a given filter function. In the ideal case, the transmission of the filter is 100% for the free spectral range of the grating, which defines the spectral separation between two successive diffraction orders, and 0% elsewhere. However, in reality, the transmission of the AOTF filter might have sidelobes that allow light from other diffraction orders to go through the filter. As a consequence of this, the light collected on the detector is a combination of diffraction orders weighted by the transmission function.\n",
    "\n",
    "In order to illustrate this, below we show an example of a realistic AOTF filter function, highlighting the wavenumber array of the main and the adjacent orders. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fe3d5f0b-b176-46c0-a403-f9143aad7077",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ideal filter function\n",
    "###########################################################################################################\n",
    "\n",
    "center_wavenumber = Measurement.VCONV[int(Measurement.NCONV[0]/2),0]  # cm^-1\n",
    "target_fwhm = 120.0         # desired main lobe FWHM in cm^-1\n",
    "num_points = 4000\n",
    "\n",
    "wavenumbers = np.linspace(center_wavenumber - 400, center_wavenumber + 400, num_points)\n",
    "\n",
    "aotf_ideal = np.zeros(num_points)\n",
    "aotf_ideal[ (wavenumbers>=center_wavenumber-target_fwhm/2) & (wavenumbers<=center_wavenumber+target_fwhm/2) ] = 1.\n",
    "\n",
    "\n",
    "# AOTF function modelled as a sinc function\n",
    "#######################################################################################\n",
    "\n",
    "# Relation between FWHM and sinc width\n",
    "width_parameter = target_fwhm / 0.885\n",
    "\n",
    "# Base sinc² profile\n",
    "sinc_arg = (wavenumbers - center_wavenumber) / (width_parameter / 2.0)\n",
    "transmission = np.sinc(sinc_arg) ** 2\n",
    "transmission /= np.max(transmission)\n",
    "\n",
    "# Estimate first side-lobe amplitude (for sinc² it's about 0.047)\n",
    "avg_sidelobe = 0.047\n",
    "desired_sidelobe = 0.1\n",
    "s = desired_sidelobe / avg_sidelobe  # scale factor for far wings\n",
    "\n",
    "# Smooth Gaussian-like envelope to enhance lobes\n",
    "delta_zero = width_parameter / 2.0\n",
    "sigma_env = delta_zero\n",
    "envelope = s + (1.0 - s) * np.exp(-((wavenumbers - center_wavenumber) / sigma_env)**2)\n",
    "\n",
    "# Apply envelope and renormalise\n",
    "aotf_sinc = transmission * envelope\n",
    "aotf_sinc /= np.max(aotf_sinc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4690049f-949a-45f0-9809-e25493ca5ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax1 = plt.subplots(1,1,figsize=(6,3))\n",
    "\n",
    "ax1.plot(wavenumbers,aotf_ideal,label='Ideal AOTF function')\n",
    "ax1.plot(wavenumbers,aotf_sinc,label='Realistic AOTF function')\n",
    "\n",
    "labels = [\"-2\",\"-1\",\"0\",\"1\",\"2\"]\n",
    "for i in range(Measurement.NORDERS_AOTF):\n",
    "    ax1.axvline(Measurement.VCONV_AOTF[0,0,i],c='black',linewidth=0.75,linestyle='--')\n",
    "    ax1.axvline(Measurement.VCONV_AOTF[-1,0,i],c='black',linewidth=0.75,linestyle='--')\n",
    "\n",
    "    ax1.text(Measurement.VCONV_AOTF[int(Measurement.NCONV[0]/2),0,i], 1.1, 'Order '+labels[i], horizontalalignment='center',\n",
    "         verticalalignment='center')\n",
    "\n",
    "ax1.set_xlabel(r'Wavenumber (cm$^{-1}$)')\n",
    "ax1.set_ylabel('AOTF transmission')\n",
    "ax1.set_facecolor('lightgray')\n",
    "ax1.legend()\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "724b1724-60be-4763-b71d-1fb57d421546",
   "metadata": {},
   "source": [
    "## 3. Defining the AOTF filter function in archNEMESIS\n",
    "\n",
    "ArchNEMESIS allows the combination of spectra from different diffraction orders, mimicking the instrumental response of an instrument combining an AOTF filter with an echelle grating.\n",
    "\n",
    "The main parameters that need to be defined are:\n",
    "- NORDERS_AOTF: Indicates the number of diffraction orders that need to be included. This number will depend on how far from the central wavelength the AOTF filter function extends to.\n",
    "- VCONV_AOTF: This indicates the convolution wavelengths or wavenumbers for each of the diffraction orders. The shape of this array must be (NCONV,NGEOM,NORDERS_AOTF). Note that NCONV must be the same size for all diffraction orders.\n",
    "- TRANS_AOTF: This indicates the transmission of the AOTF filter for each of the convolution wavelengths or wavenumbers.\n",
    "\n",
    "In order to illustrate this, we are going to use the AOTF filter function from the previous section and adapt it to the diffraction orders specified in the input files. We are going to create two cases, one with the ideal AOTF function, and one with the realistic one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db385fc7-8738-4ede-9a78-76094f9e2de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Measurement_ideal = deepcopy(Measurement)\n",
    "Measurement_sinc = deepcopy(Measurement)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0eea8205-316f-42eb-8162-3cb85502c7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_aotf_ideal = np.zeros((Measurement.NCONV.max(),Measurement.NGEOM,Measurement.NORDERS_AOTF))\n",
    "trans_aotf_sinc = np.zeros((Measurement.NCONV.max(),Measurement.NGEOM,Measurement.NORDERS_AOTF))\n",
    "for iorder in range(Measurement.NORDERS_AOTF):\n",
    "\n",
    "    #Interpolating AOTF filter function to the diffraction orders\n",
    "    trans_aotfx = np.interp(Measurement.VCONV_AOTF[:,0,iorder],wavenumbers,aotf_sinc)\n",
    "    trans_aotf_sinc[:,:,iorder] = trans_aotfx[:,np.newaxis]\n",
    "\n",
    "    trans_aotfx = np.interp(Measurement.VCONV_AOTF[:,0,iorder],wavenumbers,aotf_ideal)\n",
    "    trans_aotf_ideal[:,:,iorder] = trans_aotfx[:,np.newaxis]\n",
    "\n",
    "Measurement_ideal.TRANS_AOTF = trans_aotf_ideal\n",
    "Measurement_ideal.assess()\n",
    "\n",
    "Measurement_sinc.TRANS_AOTF = trans_aotf_sinc\n",
    "Measurement_sinc.assess()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5a561b6-5c05-4dd0-ac7c-6f1241052387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax1 = plt.subplots(1,1,figsize=(6,3))\n",
    "\n",
    "ax1.plot(wavenumbers,aotf_sinc,label='Realistic AOTF function',linewidth=0.5,c='tab:red')\n",
    "ax1.plot(wavenumbers,aotf_ideal,label='Ideal AOTF function',linewidth=0.5,c='tab:blue')\n",
    "\n",
    "labels = [\"-2\",\"-1\",\"0\",\"1\",\"2\"]\n",
    "for i in range(Measurement.NORDERS_AOTF):\n",
    "    ax1.plot(Measurement_sinc.VCONV_AOTF[:,0,i],Measurement_sinc.TRANS_AOTF[:,0,i],c='tab:red')\n",
    "    ax1.plot(Measurement_ideal.VCONV_AOTF[:,0,i],Measurement_ideal.TRANS_AOTF[:,0,i],c='tab:blue')\n",
    "    ax1.axvline(Measurement.VCONV_AOTF[0,0,i],c='black',linewidth=0.75,linestyle='--')\n",
    "    ax1.axvline(Measurement.VCONV_AOTF[-1,0,i],c='black',linewidth=0.75,linestyle='--')\n",
    "\n",
    "    ax1.text(Measurement.VCONV_AOTF[int(Measurement.NCONV[0]/2),0,i], 1.1, 'Order '+labels[i], horizontalalignment='center',\n",
    "         verticalalignment='center')\n",
    "\n",
    "ax1.set_xlabel(r'Wavenumber (cm$^{-1}$)')\n",
    "ax1.set_ylabel('AOTF transmission')\n",
    "ax1.set_facecolor('lightgray')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed79c498-d7bd-4dc0-8c3f-86c9efa63b64",
   "metadata": {},
   "source": [
    "## 4. Calculating forward model\n",
    "\n",
    "Once we have correctly defined the AOTF filter function into the Measurement class, we can compute the spectra for a solar occultation measurement just as if we were not including any AOTF function at all.\n",
    "\n",
    "In order to reconstruct the AOTF filter function, the spectra for each of the diffraction orders is weighted based on their AOTF transmissions. Then, the combined spectra is normalised. Mathematically, this is expressed as:\n",
    "\n",
    "\n",
    "$$\n",
    "R(\\lambda) = \\dfrac{\\sum_{i=1}^{NORDERS} R_i(\\lambda) \\, T_{AOTF_i}(\\lambda)}{\\sum_{i=1}^{NORDERS} T_{AOTF_i}(\\lambda)}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e093b3e-4f14-4780-ac48-4c9dfde8828a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO :: __init__ :: ForwardModel_0.py-255 :: Checking atmospheric gasses have spectroscopy data.\n",
      "WARNING :: __init__ :: ForwardModel_0.py-302 :: Not all atmospheric gasses have spectroscopy data.\n",
      "# WARNING #########################################################################\n",
      "\n",
      "The following atmospheric gasses ARE NOT PRESENT in the spectroscopy data and WILL NOT CONTRIBUTE TO OPACITY:\n",
      "\n",
      "    H2O (id 1) isotopologue 0\n",
      "    N2 (id 22) isotopologue 0\n",
      "    Ar (id 76) isotopologue 0\n",
      "    CO (id 5) isotopologue 0\n",
      "    O (id 45) isotopologue 0\n",
      "    O2 (id 7) isotopologue 0\n",
      "    O3 (id 3) isotopologue 0\n",
      "    H (id 48) isotopologue 0\n",
      "    H2 (id 39) isotopologue 0\n",
      "    He (id 40) isotopologue 0\n",
      "    CO2 (id 2) isotopologue 3\n",
      "    CO2 (id 2) isotopologue 4\n",
      "\n",
      "To deactivate this warning place a path to a line-by-line-table file for these gasses in one of the following locations (depending upon your input file type):\n",
      "\n",
      "    [HDF5 Input]\n",
      "        In the \"acsnir_fm.h5\" file, add an entry to \"/Spectroscopy/LOCATION\"\n",
      "        and update \"/Spectroscopy/NGAS\" appropriately.\n",
      "\n",
      "    [LEGACY Input]\n",
      "        Add an entry to the \"acsnir_fm.lls\" file.\n",
      "\n",
      "# END WARNING #####################################################################\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-749 :: Calculating forward models for each of the diffraction orders to reconstruct AOTF filter function\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-755 :: Calculating forward model for diffraction order 1 of 5\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-764 :: Spectral range = 6444.55810546875 to 6518.33349609375\n",
      "WARNING :: layer_split :: Layer_0.py-1438 ::  from layer_split() :: LAYHT < H(0). Resetting LAYHT\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3312 :: self.CIAX not included in calculations\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3383 :: CIRSrad :: Aerosol optical depths at  (6444.072, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3401 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3418 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-3745 :: CIRSrad :: IMODM = <PathCalc.PLANCK_FUNCTION_AT_BIN_CENTRE: 8192>\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-812 :: Convolving spectra and gradients with instrument line shape\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-755 :: Calculating forward model for diffraction order 2 of 5\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-764 :: Spectral range = 6573.44921875 to 6648.7001953125\n",
      "WARNING :: layer_split :: Layer_0.py-1438 ::  from layer_split() :: LAYHT < H(0). Resetting LAYHT\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3312 :: self.CIAX not included in calculations\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3383 :: CIRSrad :: Aerosol optical depths at  (6572.963, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3401 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3418 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-3745 :: CIRSrad :: IMODM = <PathCalc.PLANCK_FUNCTION_AT_BIN_CENTRE: 8192>\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-812 :: Convolving spectra and gradients with instrument line shape\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-755 :: Calculating forward model for diffraction order 3 of 5\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-764 :: Spectral range = 6702.3408203125 to 6779.06640625\n",
      "WARNING :: layer_split :: Layer_0.py-1438 ::  from layer_split() :: LAYHT < H(0). Resetting LAYHT\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3312 :: self.CIAX not included in calculations\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3383 :: CIRSrad :: Aerosol optical depths at  (6701.855, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3401 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3418 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-3745 :: CIRSrad :: IMODM = <PathCalc.PLANCK_FUNCTION_AT_BIN_CENTRE: 8192>\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-812 :: Convolving spectra and gradients with instrument line shape\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-755 :: Calculating forward model for diffraction order 4 of 5\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-764 :: Spectral range = 6831.23193359375 to 6909.43310546875\n",
      "WARNING :: layer_split :: Layer_0.py-1438 ::  from layer_split() :: LAYHT < H(0). Resetting LAYHT\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3312 :: self.CIAX not included in calculations\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3383 :: CIRSrad :: Aerosol optical depths at  (6830.746, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3401 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3418 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-3745 :: CIRSrad :: IMODM = <PathCalc.PLANCK_FUNCTION_AT_BIN_CENTRE: 8192>\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-812 :: Convolving spectra and gradients with instrument line shape\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-755 :: Calculating forward model for diffraction order 5 of 5\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-764 :: Spectral range = 6960.123046875 to 7039.7998046875\n",
      "WARNING :: layer_split :: Layer_0.py-1438 ::  from layer_split() :: LAYHT < H(0). Resetting LAYHT\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3312 :: self.CIAX not included in calculations\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3383 :: CIRSrad :: Aerosol optical depths at  (6959.637, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3401 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3418 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-3745 :: CIRSrad :: IMODM = <PathCalc.PLANCK_FUNCTION_AT_BIN_CENTRE: 8192>\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-812 :: Convolving spectra and gradients with instrument line shape\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-749 :: Calculating forward models for each of the diffraction orders to reconstruct AOTF filter function\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-755 :: Calculating forward model for diffraction order 1 of 5\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-764 :: Spectral range = 6444.55810546875 to 6518.33349609375\n",
      "WARNING :: layer_split :: Layer_0.py-1438 ::  from layer_split() :: LAYHT < H(0). Resetting LAYHT\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3312 :: self.CIAX not included in calculations\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3383 :: CIRSrad :: Aerosol optical depths at  (6444.072, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3401 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3418 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-3745 :: CIRSrad :: IMODM = <PathCalc.PLANCK_FUNCTION_AT_BIN_CENTRE: 8192>\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-812 :: Convolving spectra and gradients with instrument line shape\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-755 :: Calculating forward model for diffraction order 2 of 5\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-764 :: Spectral range = 6573.44921875 to 6648.7001953125\n",
      "WARNING :: layer_split :: Layer_0.py-1438 ::  from layer_split() :: LAYHT < H(0). Resetting LAYHT\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3312 :: self.CIAX not included in calculations\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3383 :: CIRSrad :: Aerosol optical depths at  (6572.963, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3401 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3418 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-3745 :: CIRSrad :: IMODM = <PathCalc.PLANCK_FUNCTION_AT_BIN_CENTRE: 8192>\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-812 :: Convolving spectra and gradients with instrument line shape\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-755 :: Calculating forward model for diffraction order 3 of 5\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-764 :: Spectral range = 6702.3408203125 to 6779.06640625\n",
      "WARNING :: layer_split :: Layer_0.py-1438 ::  from layer_split() :: LAYHT < H(0). Resetting LAYHT\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3312 :: self.CIAX not included in calculations\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3383 :: CIRSrad :: Aerosol optical depths at  (6701.855, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3401 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3418 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-3745 :: CIRSrad :: IMODM = <PathCalc.PLANCK_FUNCTION_AT_BIN_CENTRE: 8192>\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-812 :: Convolving spectra and gradients with instrument line shape\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-755 :: Calculating forward model for diffraction order 4 of 5\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-764 :: Spectral range = 6831.23193359375 to 6909.43310546875\n",
      "WARNING :: layer_split :: Layer_0.py-1438 ::  from layer_split() :: LAYHT < H(0). Resetting LAYHT\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3312 :: self.CIAX not included in calculations\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3383 :: CIRSrad :: Aerosol optical depths at  (6830.746, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3401 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3418 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-3745 :: CIRSrad :: IMODM = <PathCalc.PLANCK_FUNCTION_AT_BIN_CENTRE: 8192>\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-812 :: Convolving spectra and gradients with instrument line shape\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-755 :: Calculating forward model for diffraction order 5 of 5\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-764 :: Spectral range = 6960.123046875 to 7039.7998046875\n",
      "WARNING :: layer_split :: Layer_0.py-1438 ::  from layer_split() :: LAYHT < H(0). Resetting LAYHT\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3312 :: self.CIAX not included in calculations\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3383 :: CIRSrad :: Aerosol optical depths at  (6959.637, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3401 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3418 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-3745 :: CIRSrad :: IMODM = <PathCalc.PLANCK_FUNCTION_AT_BIN_CENTRE: 8192>\n",
      "INFO :: nemesisSOfm :: ForwardModel_0.py-812 :: Convolving spectra and gradients with instrument line shape\n"
     ]
    }
   ],
   "source": [
    "#Running forward model for ideal AOTF function\n",
    "ForwardModel = ans.ForwardModel_0(runname=runname, Atmosphere=Atmosphere,Surface=Surface,Measurement=Measurement_ideal,Spectroscopy=Spectroscopy,Stellar=Stellar,Scatter=Scatter,CIA=CIA,Layer=Layer,Variables=Variables)\n",
    "SPECONV_ideal = ForwardModel.nemesisSOfm()\n",
    "\n",
    "#Running forward model for sinc AOTF function\n",
    "ForwardModel = ans.ForwardModel_0(runname=runname, Atmosphere=Atmosphere,Surface=Surface,Measurement=Measurement_sinc,Spectroscopy=Spectroscopy,Stellar=Stellar,Scatter=Scatter,CIA=CIA,Layer=Layer,Variables=Variables)\n",
    "SPECONV_sinc = ForwardModel.nemesisSOfm()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "089c1c69-5d9f-4bb4-8438-34a729599e74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "igeom = 0\n",
    "\n",
    "fig,ax1 = plt.subplots(1,1,figsize=(10,3))\n",
    "\n",
    "ax1.plot(Measurement.VCONV[:,igeom],SPECONV_ideal[:,igeom],c='tab:blue',label='Ideal AOTF function')\n",
    "ax1.plot(Measurement.VCONV[:,igeom],SPECONV_sinc[:,igeom],c='tab:red',label='Realistic AOTF function')\n",
    "ax1.set_xlabel(r'Wavenumber (cm$^{-1}$)')\n",
    "ax1.set_ylabel('Transmission')\n",
    "ax1.set_facecolor('lightgray')\n",
    "ax1.legend()\n",
    "plt.tight_layout()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}